Gentrification Interrupted in Salford,UK: From New Deal to “Limbo‐Land” in a Contemporary Urban Periphery

This paper examines two potential lacunae in understanding low‐income residents’ experiences of contemporary state‐led gentrification via a study of neighbourhood restructuring in Salford, UK, 2004–2014. The first is the localised politics preparing the ground for neighbourhood “redevelopment” and housing demolition. The second is the blighted social landscape which emerges with the subsequent stalling of this project. A focus on “before” and “after” is adopted in order to disrupt the linear policy and “effects” temporalities that much qualitative gentrification research tends to inhabit. We see how state‐led neighbourhood restructuring does not simply displace, but carries residents from “empowerment” to abandonment and transfers them from active struggle into devitalised limbo. As such, the paper demarcates challenges and opportunities for resident mobilisation inherent in a vacillating urban renewal programme, powerful in its inception but which has since “hit the buffers” (Lees 2014, Antipode 46(4):921–947) in light of global and municipal fiscal crises.     

最后的亚马孙

在你读这篇文章的一会儿功夫里,就会有150个足球场那么大一片的巴西雨林被毁。全球化的市场大军正在入侵亚马孙平原,加速着雨林的毁灭,令最忠心耿耿的森林卫士也束手无策。     

From Big Noise to Big Data: Toward the Verification of Large Data sets for Understanding Regional Retail Flows

There has been much excitement among quantitative geographers about newly available data sets, characterized by high volume, velocity, and variety. This phenomenon is often labeled as “Big Data” and has contributed to methodological and empirical advances, particularly in the areas of visualization and analysis of social networks. However, a fourth v—veracity (or lack thereof)—has been conspicuously lacking from the literature. This article sets out to test the potential for verifying large data sets. It does this by cross‐comparing three unrelated estimates of retail flows—human movements from home locations to shopping centers—derived from the following geo‐coded sources: (1) a major mobile telephone service provider; (2) a commercial consumer survey; and (3) geotagged Twitter messages. Three spatial interaction models also provided estimates of flow: constrained and unconstrained versions of the “gravity model” and the recently developed “radiation model.” We found positive relationships between all data‐based and theoretical sources of estimated retail flows. Based on the analysis, the mobile telephone data fitted the modeled flows and consumer survey data closely, while flows obtained directly from the Twitter data diverged from other sources. The research highlights the importance of verification in flow data derived from new sources and demonstrates methods for achieving this.